Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the o...

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Bibliographic Details
Main Authors: Chan, Kit, Khadem, Saghar, Dillon, Tharam, Palade, Vasile, Singh, Jaipal, Chang, Elizabeth
Format: Journal Article
Published: IEEE 2012
Subjects:
neural network configuration
Taguchi method
traffic flow forecasting
Input patterns
neural networks
sensor data
Online Access:http://hdl.handle.net/20.500.11937/21506

Internet

http://hdl.handle.net/20.500.11937/21506

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